Language Model Security Database

The MASTER KEY framework exploits timing-based characteristics of Large Language Model (LLM) chatbot responses to infer internal defense mechanisms and automatically generate jailbreak prompts. This allows bypassing safety restrictions and eliciting responses violating usage policies, including generation of illegal, harmful, privacy-violating, and adult content. The framework utilizes a three-step process: reverse-engineering defenses via time-based analysis, creating proof-of-concept…

A vulnerability in multi-modal large language models (LLMs) allows adversaries to bypass safety mechanisms through compositional adversarial attacks. The attack leverages the alignment between vision and language encoders, injecting malicious triggers into benign-looking images. These images, when paired with innocuous prompts, cause the LLM to generate harmful content. The attack requires access only to the vision encoder (e.g., CLIP), not the LLM itself, lowering the barrier to attack.

Aligned large language models (LLMs) are vulnerable to a universal and transferable adversarial suffix attack. Appending a specific, automatically generated suffix to a wide range of prompts, even those requesting objectionable content, causes the models to generate harmful or objectionable responses instead of refusing the request. The attack's success rate is significantly higher on GPT-based models.

A prompt injection vulnerability allows attackers to manipulate the behavior of Large Language Model (LLM)-integrated applications by crafting malicious prompts that override the application's intended functionality. Attackers can achieve this by constructing prompts that cause the LLM to interpret malicious payloads as instructions, rather than data, leading to unintended actions such as data leakage, unauthorized LLM usage, or application mimicry. This vulnerability exploits the way user…

A vulnerability in vision-integrated Large Language Models (VLMs) allows an attacker to circumvent safety mechanisms through the use of adversarially crafted visual examples. A single, carefully constructed image can universally "jailbreak" the model, causing it to generate harmful content in response to a wide range of subsequent prompts, even those not included in the adversarial example's training data. This vulnerability extends beyond simple misclassification to encompass the execution of…

Large Language Models (LLMs), specifically ChatGPT versions 3.5 and 4.0, are vulnerable to prompt engineering attacks that circumvent built-in content restrictions. Attackers can craft malicious prompts, categorized into "pretending," "attention shifting," and "privilege escalation" techniques, to elicit responses containing prohibited content (e.g., instructions for illegal activities, generation of harmful content). The vulnerability stems from the LLM's inability to reliably distinguish…